{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":40,"n_cols":5,"rows_kept":40,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[12,2,12,3],[13,2,13,3],[16,2,16,3],[23,2,23,3],[23,4,23,5],[24,2,24,3],[24,4,24,5],[25,2,25,3],[27,2,27,3],[28,2,28,3],[29,2,29,3],[30,2,30,3],[31,2,31,3],[36,2,36,3],[37,2,37,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910134853503321"]
[null,null,"Titolo",null,"Computational network analysis with R : applications in biology, medicine, and chemistry / / edited by Mathtias Dehmer, Yongtang Shi, and Frank Emmert-Streib"]
[null,null,"Pubbl/distr/stampa",null,"Weinheim : , : Wiley-VCH, , [2017]"]
[{"$nulls":4},"©2017"]
[null,null,"ISBN",null,"3-527-69437-4"]
[{"$nulls":4},"3-527-69440-4"]
[{"$nulls":4},"3-527-69436-6"]
[null,null,"Descrizione fisica",null,"1 online resource (365 p.)"]
[null,null,"Collana",null,"Quantitative and network biology ; ; volume 7"]
[null,null,"Soggetti",null,"R (Computer program language)"]
[{"$nulls":4},"Medicine - Computer programs"]
[{"$nulls":4},"Application software"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Cover; Title Page; Copyright; Contents; List of Contributors; Chapter 1 Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks; 1.1 Introduction; 1.1.1 An Introduction to Omics and Systems Biology; 1.1.2 Correlation Networks in Omics and Systems Biology; 1.1.3 Network Modules and Differential Network Approaches; 1.1.4 Aims of this Chapter; 1.2 What is DiffCorr?; 1.2.1 Background; 1.2.2 Methods; 1.2.3 Main Functions in DiffCorr; 1.2.4 Installing the DiffCorr Package"]
[{"$nulls":4},"1.3 Constructing Co-Expression (Correlation) Networks from Omics Data - Transcriptome Data set1.3.1 Downloading the Transcriptome Data set; 1.3.2 Data Filtering; 1.3.3 Calculation of the Correlation and Visualization of Correlation Networks; 1.3.4 Graph Clustering; 1.3.5 Gene Ontology Enrichment Analysis; 1.4 Differential Correlation Analysis by DiffCorr Package; 1.4.1 Calculation of Differential Co-Expression between Organs in Arabidopsis; 1.4.2 Exploring the Metabolome Data of Flavonoid-Deficient Arabidopsis; 1.4.3 Avoiding Pitfalls in (Differential) Correlation Analysis; 1.5 Conclusion"]
[{"$nulls":4},"AcknowledgmentsConflicts of Interest; References; Chapter 2 Analytical Models and Methods for Anomaly Detection in Dynamic, Attributed Graphs; 2.1 Introduction; 2.2 Chapter Definitions and Notation; 2.3 "]
[{"$nulls":4},"Anomaly Detection in Graph Data; 2.3.1 Neighborhood-Based Techniques; 2.3.2 Frequent Subgraph Techniques; 2.3.3 Anomalies in Random Graphs; 2.4 Random Graph Models; 2.4.1 Models with Attributes; 2.4.2 Dynamic Graph Models; 2.5 Spectral Subgraph Detection in Dynamic, Attributed Graphs; 2.5.1 Problem Model; 2.5.2 Filter Optimization; 2.5.3 Residuals Analysis in Attributed Graphs"]
[{"$nulls":4},"2.6 Implementation in R2.7 Demonstration in Random Synthetic Backgrounds; 2.8 Data Analysis Example; 2.9 Summary; Acknowledgments; References; Chapter 3 Bayesian Computational Algorithms for Social Network Analysis; 3.1 Introduction; 3.2 Social Networks as Random Graphs; 3.3 Statistical Modeling Approaches to Social Network Analysis; 3.3.1 Exponential Random Graph Models (ERGMs); 3.3.2 Latent Space Models (LSMs); 3.4 Bayesian Inference for Social Network Models; 3.4.1 R-Based Software Tools; 3.5 Data; 3.5.1 Bayesian Inference for Exponential Random Graph Models"]
[{"$nulls":4},"3.5.2 Bayesian Inference for Latent Space Models3.5.3 Predictive Goodness-of-Fit (GoF) Diagnostics; 3.6 Conclusions; References; Chapter 4 Threshold Degradation in R Using iDEMO; 4.1 Introduction; 4.2 Statistical Overview: Degradation Models; 4.2.1 Wiener Degradation-Based Process; 4.2.1.1 Lifetime Information; 4.2.1.2 Log-Likelihood Function; 4.2.2 Gamma Degradation-Based Process; 4.2.2.1 Lifetime Information; 4.2.2.2 Log-Likelihood Function; 4.2.3 Inverse Gaussian Degradation-Based Process; 4.2.3.1 Lifetime Distribution; 4.2.3.2 Log-Likelihood Function; 4.2.4 Model Selection Criteria"]
[{"$nulls":4},"4.2.5 Choice of   (t)"]
[null,"2.","Record Nr.",null,"UNINA9910821492703321"]
[null,null,"Titolo",null,"Race, education, and reintegrating formerly incarcerated citizens : counterstories and counterspaces / / edited by John R. Chaney, Joni Schwartz"]
[null,null,"Pubbl/distr/stampa",null,"Lanham, Maryland : , : Lexington Books, , 2017"]
[{"$nulls":4},"©2017"]
[null,null,"ISBN",null,"1-4985-4092-9"]
[null,null,"Descrizione fisica",null,"1 online resource (214 pages) : illustrations"]
[null,null,"Collana",null,"Critical Perspectives on Race, Crime, and Justice"]
[null,null,"Disciplina",null,"364.80973"]
[null,null,"Soggetti",null,"Prisoners - Education - United States"]
[{"$nulls":4},"Ex-convicts - Education - United States"]
[{"$nulls":4},"Criminals - Rehabilitation - United States"]
[{"$nulls":4},"Prisoners - Deinstitutionalization - United States"]
[{"$nulls":4},"Education and crime - United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]